Anatomy Detection and Localization in 3D Medical Images

Abstract

This chapter discusses the use of regression forests for the automatic detection and simultaneous localization of multiple anatomical regions within computed tomography (CT) and magnetic resonance (MR) three-dimensional images. Important applications include: organ-specific tracking of radiation dose over time; selective retrieval of patient images from radiological database systems; semantic visual navigation; and the initialization of organ-specific image processing operations. We present a continuous parametrization of the anatomy localization problem, which allows it to be addressed effectively by multivariate random regression forests (Chap. 5). A single pass of our probabilistic algorithm enables the direct mapping from voxels to organ location and size, with training focusing on maximizing the confidence of output predictions. As a by-product, our method produces salient anatomical landmarks, i.e. automatically selected “anchor” regions which help localize organs of interest with high confidence. This chapter builds upon the work in Criminisi et al., in MICCAI workshop on medical computer vision: recognition techniques and applications in medical imaging, 2010 and in Pauly et al., Proc medical image computing and computer assisted intervention, 2011 and demonstrates the flexibility of forests in dealing with both CT and multi-channel MR scans. Quantitative validation is performed on two ground truth labeled datasets: (i) a database of 400 highly variable CT scans, and (ii) a database of 33 full-body, multi-channel MR scans. In both cases localization errors are reduced and results are more stable than those from more conventional atlas-based registration approaches. The simplicity of the regressor’s context-rich visual features yield typical run-times of only 4 seconds per scan on a standard desktop. This anatomy recognition algorithm has now received FDA approval and is part of Caradigm’s Amalga (www.caradigm.com).